Deep Residual Learning For Computer Vision In Healthcare
Recent advances in artificial intelligence and computer vision have had major implications for the healthcare industry, facilitating research, diagnosis, and treatment. Learn about computer vision and why it is important for healthcare, and see examples of medical applications. We will also focus on deep residual networks and their impact on computer vision in healthcare.
In this article:
- What is computer vision
- The need for computer vision in healthcare
- Medical applications for computer vision
- Deep residual learning for healthcare with MissingLink
What Is Computer Vision?
Computer vision is a form of Artificial Intelligence (AI) that replicates human sight and identifies objects. It involves training a machine to segment and classify objects in an image of a certain class. Basic computer vision relies on standard Machine Learning (ML) algorithms to process images, while advanced computer vision leverages Deep Learning (DL) technology to process images even faster and at scale.
Deep learning involves neural networks that are modeled after the way the human brain functions, with “neurons” organized in layers. Neural networks identify patterns in the pixels that make up an image and make assumptions based on context. Over time, advancements in computer vision have led to the development of the deep learning algorithm known as a Convolution Neural Network (CNN).
Convolutional neural networks can distinguish between different objects in an input image and identify high-level features like edges. CNNs require less pre-processing than other classification algorithms while understanding images in greater detail.
However, neural networks, including CNNs, also have their challenges, such as the vanishing gradient problem, which occurs when the gradient in the image is too fine and the network cannot distinguish the change. For example, the difference in tone between pixels may be so small that the network is unable to assign them different values. This could prevent further training of the neural network.
One solution for the vanishing gradient problem is a residual neural network (ResNet), which is a group of shallow networks that simplify the training process with the use of residual connections, also known as skip connections. The residual network reroutes the input so that the next layer will learn from the previous layer, adjusting the weights and muting the upstream layer.
Deeper networks, with more than 25 or 30 layers, can have a higher level of training error than shallow networks. A residual learning framework can thus improve accuracy for deeper networks. The Deep Residual Learning network, which was developed by researchers at Microsoft Research, had 152 layers. This kind of network can help achieve the depth of representations needed for advanced computer vision tasks.
The Need For Computer Vision In Healthcare
Computer vision presents a potentially life-saving tool in healthcare, accelerating the process of extracting information from medical imaging, and improving the accuracy of medical exams. Combined with deep learning, computer vision saves time when analyzing and diagnosing diseases, and allows doctors to focus more on caring for their patients.
Many diseases or injuries are time-sensitive, and the patients simply cannot afford to wait for manual analysis. By allowing machines to handle otherwise time-consuming tasks, computer vision saves time, enables early diagnosis and immediate treatment, and helps cut the costs of screening.
Computer vision is also useful for improving the accuracy of medical test results. Medical images are often grainy and difficult to understand, with small, intricate patterns and subtle differences in tone. Computer vision algorithms enable faster and more accurate interpretation of these images, which makes for more precise diagnoses than is possible with a human practitioner alone.
Medical Applications For Computer Vision
There are many applications for computer vision in healthcare ranging from research and diagnosis to treatment and surgery. Advancements in computed image processing and pattern recognition, notably deep neural networks and residual learning, have led to an increase in the adoption of these technologies at hospitals and medical research facilities.
Examples of medical applications for computer vision include:
Computer vision can help identify cancerous cells and tumors in images or biopsy results. A trained neural network can detect tissue abnormalities more effectively than doctors. Some of the most important breakthroughs have been in identifying skin cancer, with computer vision algorithms providing a fast and thorough analysis for dermatologists.
Medical researchers and companies that develop drugs and medical devices use computer vision to identify trends in medical imaging data. Clinical trials use anonymized scans of patients to find patterns in disease progression and learn how to prevent diseases. Computer vision accelerates the process and makes connections that a human researcher may not notice.
By understanding the patterns of injuries and tumors, including how they may progress, computer vision with a deep learning platform can help predict future development based on test results. For example, you can combine the image segmentation capabilities of computer vision with modeling and prototyping technologies to build predictive models from medical imaging like MRI. Medical practitioners can potentially use predictive analytics to assist them in consultations and track the status of a patient in real time.
Perhaps the most obvious use for computer vision is in medical imaging, which requires the segmentation of different organ and tissue types. To enable the comprehensive analysis of the region being scanned, medical practitioners can integrate information from a variety of diagnostic imaging techniques, such as magnetic resonance imaging (MRI), computed tomography (CT), radiography (X-ray), and ultrasound. This approach, known as image coregistration, is helpful for both a qualitative visual assessment and a quantitative analysis of multiple parameters.
Deep Residual Learning For Healthcare With MissingLink
Deep residual learning can help you realize the potential of computer vision algorithms for a growing number of healthcare applications. From conducting research more efficiently to diagnosing and treating cancer quickly and accurately, advances in AI, represented by the deep residual network, can help cut costs and deliver results sooner.
The MissingLink deep learning framework helps you manage multiple deep learning experiments. You can schedule, automate, and record your experiments with MissingLink to save time and money.
Use the MissingLink platform to:
- Automatically scale ResNet across multiple machines, on-premise or in the cloud
- Automatically run deep learning tasks in a defined cluster of machines
- Continuously run experiments and shut down cloud machines when the tasks are completed to ensure optimal resource utilization and avoid idle time
- Track and share results
- Manage a large number of experiments and easily sync large datasets to training machines